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The authors assess incidence and mortality rates of two cancer types in relation to trihalomethane pollutant concentrations in drinking water.

The authors looked at different factors that influence specific gravity in beverages, including sweetener used, caffeine, carbonation, and sodium.

Lung cancer is highly fatal, largely due to late diagnoses, but early detection can greatly improve survival. This study developed three models to enhance early diagnosis: an MLP for clinical data, a CNN for imaging data, and a hybrid model combining both.

This study investigated how well food labels from 280 different brands across multiple food and drink categories in India and the US adhered to recommended nutritional labeling standards as outlined by the Codex Alimentarius.

Current osteosarcoma (OS) treatments rely on surgery and chemotherapy, but drug resistance remains a major challenge that lowers patient survival rates. Ferroptosis, a form of regulated cell death, has shown promise in cancer therapy but is not well understood in OS. This study explores the use of Ferroptosis in OS.

The authors investigated whether the physician-patient relationship affected patient perceptions and treatment adherence.

Here, the researchers investigated how exposure to active versus passive music affects a teenager's ability to perform a challenging task, namely a Sudoku puzzle, under stressful conditions. Following testing 75 high school teenagers split into two group, the researchers found that singing in a choir (active music) yielded a greater improvement in performance compared to passive listening for brief time periods.

Kalirin is a guanine nucleotide exchange factor (GEF) for the GTPase RAC1, linked to schizophrenia and Alzheimer’s Disease. It plays a crucial role in synaptic plasticity by regulating dendritic spine formation and actin cytoskeleton remodeling, which are essential for creating new synapses. Authors developed two novel compounds targeting kalirin, confirming that predictive modeling can indicate biological activity.

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.